Implement CMA-ES module #102
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Parent
#99
What to build
part-of: #99
Create a standalone CMA-ES optimizer module. Full (not separable) CMA-ES with state object holding mean, covariance matrix, step-size (σ), evolution paths. Interface: init, sample λ candidates, update distribution from ranked results. Auto-sized λ = 4 + floor(3 × ln(n)). No game dependency — pure optimizer.
Acceptance criteria
initCma(n)creates a valid CMA state for n dimensionssample()produces λ candidate weight vectors (λ auto-sized)update()accepts ranked candidates and updates the distributionBlocked by
None — can start immediately.